Signal analysis is a key technology used in industry, medical field, astronomy, nuclear engineering, sub-band coding, optics, turbulence, earthquake prediction, etc. In signal analysis, an information associated with a signal is analyzed to identify the functionality of a system or a device. For example, by analyzing a cardiac signal of a patient, cardiac health of a patient can be assessed. However, the information about the signal is distributed across both time and frequency domain. The information distributed across the time domain can be analyzed using time domain analysis. For example, the cardiac signal rate can be different at different time interval. Typically, the frequency domain of a signal can be analyzed using techniques, for example, Fast Fourier Transform (FFT), Discrete Fourier Transform (DFT), etc. However, while analyzing the time domain of the signal, the information about the frequency domain may be lost and while analyzing the frequency domain of the signal, the information about the time domain may be lost.
Conventional methods are able to analyze either the frequency domain of the signal or the time domain of the signal. But, there is a challenge in analyzing both the frequency domain and time domain of a signal simultaneously. Hence wavelet transforms are used for analyzing the time domain and frequency domain of a signal simultaneously. In wavelet transform, a mother wavelet plays an important role in analyzing the signal. The mother wavelet is a prototype function for generating various wavelets with varying scale. But, selecting a mother wavelet is a challenging task because a set of properties of the mother wavelet and a set of properties of the signal to be analyzed should be matched carefully. The conventional methods mostly rely on manual selection of mother wavelets and some of the automated methods may be inefficient in analyzing the signal in an efficient manner.